Recognizing carbon-saving behaviors from images

ABSTRACT

The present specification discloses a data processing method, apparatus, and equipment. The method includes: obtaining an image sent by a user, wherein the image to is collected by the user for a merchant; identifying carbon-saving behavior of the merchant, comprising processing the image using a trained image recognition model; and determining, based on the carbon-saving behavior, a value representing an amount of carbon saved by the merchant.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of PCT Application No.PCT/CN2018/111852, filed on Oct. 25, 2018, which claims priority toChinese Patent Application No. 201711334828.0, filed on Dec. 14, 2017,and each application is hereby incorporated by reference in itsentirety.

TECHNICAL FIELD

The present specification relates to the field of computer technologies,and in particular, to a data processing method, apparatus, andequipment.

BACKGROUND

Carbon emission is a general term or abbreviation for greenhouse gasemission. People's daily life can directly or indirectly cause carbonemission such as car exhaust emission, thermal power generation, the useof disposable products, etc. With increase of carbon emission, thedegree of damage to the environment where people live is increasinglysevere.

Currently, people are reducing carbon emission in many ways, such asdeveloping and promoting cleaner electric vehicles, building more windand hydroelectric power stations, and manufacturing and using moreenvironment-friendly consumables. Currently, however, there are noeffective criteria for carbon-saving quantification that can enablepeople to understand the carbon-saving condition in real life. Thus, itis difficult to carry out more effective subsequent carbon-saving workbased on the carbon-saving condition in real life.

Therefore, how to quantify carbon-saving effectively in people's dailylife is an urgent problem to be alleviated.

SUMMARY

The present specification provides a data processing method to alleviatethe problem of ineffective quantification of carbon-saving in theexisting technology.

The present specification provides a data processing method, including:obtaining an image to be identified sent by a user, where the image tobe identified is collected by the user for a merchant; identifying theimage to be identified by using a pre-trained image identificationmodel, to identify carbon-saving behavior of the merchant; anddetermining, based on the carbon-saving behavior, a value representing acarbon-saving amount saved by the merchant.

The present specification provides a data processing apparatus toalleviate the problem of ineffective quantification of carbon-saving inthe existing technology.

The present specification provides a data processing apparatus,including: an acquisition module, configured to obtain an image to beidentified sent by a user, wherein the image to be identified iscollected by the user for a merchant; an identification module,configured to identify the image to be identified by using a pre-trainedimage identification model to determine carbon-saving behavior of themerchant; and a determining module, configured to determine, based onthe carbon-saving behavior, a value representing a carbon-saving amountsaved by the merchant.

The present specification provides a data processing equipment toalleviate the problem of ineffective quantification of carbon-saving inthe existing technology.

The present specification provides a data processing equipment,including one or more memories and processors, where the memory stores aprogram that can be executed by the one or more processors to performthe following steps: obtaining an image to be identified sent by a user,where the image to be identified is collected by the user for amerchant; identifying the image to be identified by using a pre-trainedimage identification model, to identify carbon-saving behavior of themerchant; and determining, based on the carbon-saving behavior, a valuerepresenting a carbon-saving amount saved by the merchant.

The present specification provides a data processing method to alleviatethe problem of ineffective quantification of carbon-saving in theexisting technology.

The present specification provides a data processing method, including:collecting an image to be identified for a merchant; and sending theimage to be identified to a server, so the server identifies the imageto be identified by using a pre-trained image identification model toidentify carbon-saving behavior of the merchant, and determines, basedon the carbon-saving behavior, a value representing a carbon-savingamount saved by the merchant.

The present specification provides a data processing apparatus toalleviate the problem of ineffective quantification of carbon-saving inthe existing technology.

The present specification provides a data processing apparatus,including: a collecting module, configured to collect an image to beidentified for a merchant; and a sending module, configured to send theimage to be identified to a server, so the server identifies the imageto be identified by using a pre-trained image identification model toidentify carbon-saving behavior of the merchant, and determines, basedon the carbon-saving behavior, a value representing a carbon-savingamount saved by the merchant.

The present specification provides a data processing equipment toalleviate the problem of ineffective quantification of carbon-saving inthe existing technology.

The present specification provides a data processing equipment,including one or more memories and processors, where the memory stores aprogram that can be executed by the one or more processors to performthe following steps: collecting an image to be identified for amerchant; and sending the image to be identified to a server, so theserver identifies the image to be identified by using a pre-trainedimage identification model to identify carbon-saving behavior of themerchant, and determines, based on the carbon-saving behavior, a valuerepresenting a carbon-saving amount saved by the merchant.

The following beneficial effects can be achieved by using at least oneof the previously described technical solutions used in the presentspecification:

In one or more implementations of the present specification, after animage to be identified collected by a user for a merchant is obtained,the image to be identified can be identified by using a pre-trainedimage identification model to identify carbon-saving behavior of themerchant, and a value representing a carbon-saving amount saved by themerchant can be determined based on the identified carbon-savingbehavior.

The carbon-saving behavior of the merchant can be identified based onthe image to be identified collected by the user for the merchant, andthe carbon-saving of the merchant can be effectively quantified based onthe identified carbon-saving behavior of the merchant. Therefore, themerchant can understand the actual carbon-saving condition of themerchant based on the quantified value of carbon-saving, and caneffectively carry out the subsequent carbon-saving work based on theknown actual carbon-saving condition. These actions have positiveimpacts on energy conservation and emission reduction in the society.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings described here are used to provide a furtherunderstanding of the present specification and constitute a part of thepresent specification. The illustrative implementations of the presentspecification and descriptions thereof are intended to describe thepresent specification, and do not constitute limitations to the presentspecification. In the accompanying drawings:

FIG. 1 is a schematic diagram illustrating a data processing process,according to an implementation of the present specification;

FIG. 2 is a schematic diagram illustrating image collection by a userfor a merchant, according to an implementation of the presentspecification;

FIG. 3 is a schematic diagram illustrating an entire data processingprocess, according to an implementation of the present specification;

FIG. 4 is a schematic diagram illustrating a data processing apparatus,according to an implementation of the present specification;

FIG. 5 is a schematic diagram illustrating a data processing apparatus,according to an implementation of the present specification;

FIG. 6 is a schematic diagram illustrating a data processing equipment,according to an implementation of the present specification;

FIG. 7 is a schematic diagram illustrating a data processing equipment,according to an implementation of the present specification.

DESCRIPTION OF IMPLEMENTATIONS

In the present specification, a user can collect an image of a merchantand send the collected image to a server by using an end-user equipment,so the server quantifies carbon-saving behavior of the merchant based onthe image sent by the user. Information included in the image andrelated to the carbon-saving behavior of the merchant can be collectedby the user actively for the carbon-saving behavior of the merchant. Orwhen the user is active in the merchant, items related to thecarbon-saving behavior of the merchant are incidentally collected in theprocess of collecting other information. For example, when a user ishaving a meal in the merchant, non-disposable chopsticks are collectedwhen the user takes a photo of the food. The non-disposable chopsticksmentioned here are the items related to the carbon-saving behavior ofthe merchant.

The motivation of the user for helping the merchant to increase thequantified value of the carbon-saving behavior in a deceptive way isrelatively low, so the possibility of the merchant to cheat in order toincrease the quantified value of the carbon-saving behavior can beeffectively reduced by using the image collected by the user for themerchant to quantify the carbon-saving behavior of the merchant.

In the present specification, an execution body for quantifying of themerchant can be an end-user equipment, a server, or other equipments. Tofacilitate description of the data processing method provided in thepresent specification, a server is used as an example in the followingdescription.

To enable a person skilled in the art to better understand technicalsolutions in one or more implementations of the present specification,the following clearly describes the technical solutions in one or moreimplementations of the present specification with reference to theaccompanying drawings in one or more implementations of the presentspecification. Apparently, the described implementations are merely apart rather than all of the implementations of the presentspecification. All other implementations obtained by a person ofordinary skill in the art without creative efforts shall fall within theprotection of the present specification, based on the implementations ofthe present specification.

FIG. 1 is a schematic diagram illustrating a data processing process,according to one implementation of the present specification. Theprocess includes the following steps:

S100: Obtain an image to be identified sent by a user.

In the present specification, the user can collect an image of amerchant to obtain an image to be identified, and send the image to beidentified to a server, so the server can identify the obtained image tobe identified. The image to be identified mentioned here can refer to animage that is collected by the user and includes information related tocarbon-saving behavior of the merchant. The image to be identified canbe in a video form or can be in a picture form. The carbon-savingbehavior mentioned here can be divided into two types: One type refersto a merchant takes positive actions to promote carbon-saving such asusing non-disposable tableware and environment-friendly furniture, andthe other refers to the merchant takes negative actions that do notpromote carbon-saving such as using disposable tableware and excessivecoal burning. Image collection conducted by the user for thecarbon-saving behavior of the merchant can be shown in FIG. 2.

FIG. 2 is a schematic diagram illustrating image collection by a userfor a merchant, according to an implementation of the presentspecification.

Assume that when the user is having a meal in a merchant and finds thattableware provided by the merchant is non-disposable, the user cancollect, by using a mobile phone, an image of the non-disposabletableware provided by the merchant, to obtain an image to be identified,and send the image to be identified to the server for identification.

S102: Identify the image to be identified by using a pre-trained imageidentification model to identify carbon-saving behavior of the merchant.

After obtaining the image to be identified sent by the user by using anend-user equipment, the server can identify the image to be identifiedby using the pre-trained image identification model. The imageidentification model can be obtained manually based on a large quantityof collected training samples. The image identification model mentionedhere can use common identification algorithms such as MobileNet andFaster RCNN. The used identification algorithms are not limited here.

It can be seen from step S100 that the carbon-saving behavior of themerchant can be divided into two types. Therefore, in the presentspecification, the server can identify a specific type of thecarbon-saving behavior of the merchant from the obtained image to beidentified by using this image identification model. For example, whenthe server identifies the presence of a chopstick sterilizer from theimage to be identified by using the pre-trained image identificationmodel, the server can determine that the carbon-saving behavior of themerchant is positive behavior that promotes carbon-saving.

In addition to determining the type of the carbon-saving behavior of themerchant by using the image to be identified sent by the user, theserver can also determine a specific form of the carbon-saving behaviorof the merchant. For example, when the server identifies anenergy-saving light bulb from the image to be identified by using theimage identification model, the server can determine that a specificform of carbon-saving behavior implemented by restaurant is positivebehavior of using an energy-saving light bulb. When the serveridentifies a chopstick sterilizer from the image to be identified byusing the image identification model, the server can determine that thespecific form of the carbon-saving behavior of the restaurant ispositive behavior of providing non-disposable chopsticks (because, ingeneral, the use of the chopstick sterilizer by the restaurant indicatesthat the restaurant provides non-disposable chopsticks, while providingnon-disposable chopsticks indicates that the restaurant increases thecarbon-saving amount).

For another example, when the server identifies bamboo furniture fromthe image to be identified by using the image identification model, theserver can determine that the specific form of the carbon-savingbehavior of the merchant is positive behavior of using bamboo furniture(in general, bamboos grow faster than trees, so the use of bamboofurniture can reduce the felling of trees, decreasing the felling oftrees increases the absorption of greenhouse gases by trees which inturn improves the carbon saving from the side).

In the present specification, if the server is unable to identify thecarbon-saving behavior of the merchant from the obtained image to beidentified by using the pre-trained image identification model, theserver can send a labeling request to the user, so the user labels theimage to be identified that was sent to the user. The server candetermine the carbon-saving behavior of the merchant based on thelabeled information sent by the user.

For example, assume that the user collects an image of the chopstickssterilizer and sends the obtained image to be identified to the server.When the server is unable to identify the carbon-saving behavior of themerchant from the image to be identified by using the pre-trained imageidentification model, the server can send a labeling prompt to the user.After seeing the labeling prompt sent by the server, the user can sendthe labeled information of the chopsticks sterilizer to the server byusing the end-user equipment, so the server determines the carbon-savingbehavior of the merchant based on the labeled information (the type ofthe carbon-saving behavior of the merchant, the specific form ofcarbon-saving behavior, etc.). For example, when the received labeledinformation sent by the user is disposable chopsticks, the server candetermine that the carbon-saving behavior of the merchant is negativebehavior that does not promote carbon-saving and is specifically in theform of disposable tableware.

Usually, the previous image identification model is manually pre-trainedby using a large quantity of training samples. These training samplesare usually labeled manually. Therefore, if the image identificationmodel needs to be further adjusted, some training samples need to bedetermined by manual labeling again. This will greatly consume laborcosts and reduce the efficiency of model training.

In the present specification, because the image identification model canbe trained by using the labeled information sent by the user, it'sequivalent to the user to complete labeling the training samples. Assuch, the identification ability of the image identification model isimproved constantly, the labor costs of the image identification modelare greatly reduced, and the efficiency of model training is improved.

S104: Determine, based on the carbon-saving behavior, a valuerepresenting a carbon-saving amount saved by the merchant.

After determining the carbon-saving behavior of the merchant, the servercan determine the value representing the carbon-saving amount saved bythe merchant that matches the carbon-saving behavior. When thecarbon-saving behavior of the merchant is positive behavior thatpromotes carbon-saving, the value representing the carbon-saving amountsaved by the merchant can be a positive number, and when thecarbon-saving behavior of the merchant is negative behavior that doesnot promote carbon-saving, the value representing the carbon-savingamount saved by the merchant can be a negative number.

In the present specification, the value representing the carbon-savingamount saved by the merchant can be in the form of bonus point. In otherwords, the server can determine, based on the carbon-saving behavior ofthe merchant, a carbon-saving bonus point representing the carbon-savingamount saved by the merchant. The server can add the determinedcarbon-saving bonus point to a carbon-saving account of the merchant.The carbon-saving account mentioned here can be opened in advance by themerchant. The merchant can submit merchant information to the server inadvance to apply for a carbon-saving account. The server can review themerchant information submitted by the merchant and, after determiningthat the merchant information submitted by the merchant has beenreviewed, open the carbon-saving account for the merchant.

After determining the carbon-saving behavior of the merchant, the servercan further determine the type of the carbon-saving behavior of themerchant, and then add a carbon-saving bonus point that corresponds tothe type to the carbon-saving account of the merchant. For example,assume that when the server determines, based on the identifiedcarbon-saving behavior, that the carbon-saving behavior of the merchantis positive behavior that promotes carbon-saving, the server can add thecarbon-saving bonus point that corresponds to the positive behavior tothe carbon-saving account of the merchant. When the server determinesthat the carbon-saving behavior of the merchant is negative behaviorthat does not promote carbon-saving, the server can deduct thecarbon-saving bonus point that corresponds to the negative behavior fromthe carbon-saving account of the merchant.

After determining the carbon-saving behavior of the merchant, the servercan also add, based on the specific form of the carbon-saving behavior,the carbon-saving bonus point that matches the specific form to thecarbon-saving account of the merchant. In other words, differentspecific forms can correspond to different carbon-saving bonus points.When the merchant adopts different forms of carbon-saving behavior,different carbon-saving bonus points can be obtained. For example, whenthe server determines that the specific form of the carbon-savingbehavior of the merchant is use of a chopsticks sterilizer, thecarbon-saving bonus point that corresponds to this form can be added tothe carbon-saving account of the merchant. For another example, when theserver determines that the specific form of the carbon-saving behaviorof the merchant is use of disposable tableware, the carbon-saving bonuspoint that corresponds to this form can be deducted from thecarbon-saving account of the merchant.

After determining the specific form of the carbon-saving behavior of themerchant, the server can also determine, by using the specific form,predetermined algorithm and other information, the carbon-saving bonuspoint that needs to be added to the carbon-saving account of themerchant. The other information mentioned here can refer to informationsuch as a credit rating of the user and number of good reviews oncarbon-saving by the merchant. When the server determines, based on animage to be identified sent by the user, that carbon-saving behavior ofa merchant is positive behavior that promotes carbon-saving, it can beconsidered as one good review on carbon-saving by the merchant.

Certainly, the server can also determine the carbon-saving bonus pointin another way and add the point to the carbon-saving account of themerchant. Details are omitted here.

While adding the determined carbon-saving bonus point to thecarbon-saving account of the merchant, the server can also determine thecontribution level of the user who sends the image to be identified, andthen adding the contribution level to an account of the user, and/orsending a virtual item that corresponds to the contribution level to theuser.

The server can determine the contribution degree of the user in severalways. For example, the positive carbon-saving behavior can includeseveral specific forms, and different specific forms correspond todifferent contribution degrees. Therefore, the server can determine acorresponding contribution degree based on a determined specific form ofcarbon-saving behavior, or determine a corresponding contribution degreebased on the credit rating of the user. Certainly, the server can alsodetermine the contribution degree of the user in another way. Detailsare omitted here.

There can be many forms of the previous contribution degrees, such asbonus point form and red packet form. The previous virtual item can alsohave many forms, for example, the server can issue coupons to the user,or some service VIP membership to the user based on a time limit.

To further facilitate the positive carbon-saving behavior of themerchant, the server can provide many convenient services to themerchant based on the carbon-saving bonus point in the carbon-savingaccount of the merchant. For example, when the server determines thatthe carbon-saving bonus point in the carbon-saving account of themerchant exceeds a specified score or reaches a specified ranking, theserver can display the information of the merchant on the merchantrecommendation home page to further promote the merchant. For anotherexample, when the carbon-saving bonus point in the carbon-saving accountof the merchant exceeds the specified score, the loan limit of themerchant can be increased. Certainly, the server can provide many otherconvenient services to the merchant based on the carbon-saving bonuspoint of the merchant. Details are omitted here.

It can be seen from the previous method that the carbon-saving behaviorof the merchant can be identified based on the image to be identifiedcollected by the user for the merchant, and the carbon-saving of themerchant can be effectively quantified based on the identifiedcarbon-saving behavior of the merchant. Therefore, the merchant canunderstand the actual carbon-saving condition of the merchant based onthe quantified value of carbon-saving, and can effectively carry out thesubsequent carbon-saving work based on the known actual carbon-savingcondition. These actions have positive impacts on energy conservationand emission reduction in the society.

Furthermore, the server can train and adjust the image identificationmodel by using the labeled information sent by the user and the image tobe identified from which the carbon-saving behavior is not identified.As such, the labor costs are greatly reduced and the efficiency of themodel training is improved.

In addition, the carbon-saving behavior of the merchant enables themerchant to obtain the corresponding carbon-saving bonus point, and theserver can provide convenience services for the merchant based on thecarbon-saving bonus point in the carbon-saving account of the merchant.Therefore, with the popularization of the carbon-saving account amongthe merchants and the incentive mechanism brought by the carbon-savingbonus point in the carbon-saving account, the positive carbon-savingbehavior can be promoted more effectively, which has more positiveimpacts on the living environment.

In the present specification, the server needs to quantify thecarbon-saving of the merchant based on the image to be identified sentby the user. Therefore, after receiving the image to be identified sentby the user in step S100 shown in FIG. 1, the server needs to determinewhich merchant is involved in the image to be identified, anddetermines, based on the image to be identified, a value representing acarbon-saving amount saved by the merchant. In the presentspecification, there can be many ways for the server to determine themerchant that corresponds to the image to be identified. For example,when the user rates a merchant by using a mobile phone, the user cansend the collected image to be identified to the server by using therating page of the merchant. As such, the server adds, based on theimage to be identified, the determined carbon-saving bonus point to thecarbon-saving account of the merchant rated by the user.

For another example, the user can send the collected image to beidentified to the server when making electronic payment. The server canadd, based on the identified carbon-saving behavior from the image to beidentified, the carbon-saving bonus point that corresponds to thecarbon-saving behavior to the carbon-saving account of the merchant thatthe user pays to.

For another example, when collecting the carbon-saving behavior of themerchant, the end-user equipment can determine location information onthe basis of collecting carbon-saving behavior, and send the collectedimage to be identified and the determined location information to theserver. The server can add the carbon-saving bonus point determinedbased on the image to be identified to the carbon-saving account of amerchant that corresponds to the location information. Certainly, themerchant that corresponds to the previous image to be identified canalso be determined in another way. Details are omitted here.

In the present specification, the end-user equipment can send thecollected image to be identified, a merchant identifier, and thelocation information that collection of the image to be identified isbased to the server. The server can identify the image to be identifiedwhen determining that the location information matches the location of amerchant that corresponds to the merchant identifier, and add, based onthe identified carbon-saving behavior, the carbon-saving bonus pointthat corresponds to the carbon-saving behavior to the carbon-savingaccount of the merchant.

The purpose is to prevent some merchants from cheating by collectingirrelevant images to be identified, like collecting images to beidentified of other merchants. It can be seen from the previous methodthat even if some merchants collect the images to be identified of othermerchants to cheat, the server adds, based on the location informationthat collection of the images to be identified is based, the determinedcarbon-saving bonus point to the carbon-saving account of the merchantthat corresponds to the location information, this effectively reducesthe possibility of fraud by the merchant.

In the present specification, it is possible for the server to wronglydetermine the carbon-saving behavior of the merchant by using thelabeled information because the previous labeled information obtained bythe server is determined by the user subjectively. The serverdetermines, based on the carbon-saving behavior determined by using thewrong labeled information, the value representing the carbon-savingamount saved by the merchant, which can cause loss to the merchant oranother merchant.

To reduce the adverse impacts of the previous problem, in the presentspecification, when the server needs to determine the carbon-savingbehavior of the merchant based on the labeled information sent by theuser, the server can multiply a reference value that corresponds to thecarbon-saving behavior by a determined confidence coefficient, anddetermine the product of the two as the value representing thecarbon-saving amount saved by the merchant.

The server can determine the confidence coefficient in many ways. Forexample, different carbon-saving behavior (or specific forms ofcarbon-saving behavior) can correspond to different confidencecoefficients. The server can determine a corresponding confidencecoefficient based on the determined carbon-saving behavior of themerchant (or a specific form of carbon-saving behavior), and thendetermine, based on the confidence coefficient and the reference valuethat corresponds to the carbon-saving behavior, the value representingthe carbon-saving amount saved by the merchant.

The confidence coefficient mentioned here can be determined manually.For those images to be identified that the server cannot identify thecarbon-saving behavior (or the specific form of carbon-saving behavior)by using the image identification model, a server administrator canfirst determine, based on the labeled information sent by the user, thecarbon-saving behavior (or the specific form of carbon-saving behavior)labeled by the user for these images to be identified, and manuallyidentify the images to be identified that correspond to the differentcarbon-saving behavior (or the specific forms of carbon-saving behavior)labeled by the user, to determine an accurate rate of user labeling thecarbon-saving behavior (or the specific form), then determine, based onthe determined accurate rate, the confidence coefficient thatcorresponds to the carbon-saving behavior (or the specific form ofcarbon-saving behavior).

Certainly, the server not only can determine the confidence coefficientbased on the determined carbon-saving behavior (or the specific form ofcarbon-saving behavior), but also can determine the confidencecoefficient based on at least one of user information or merchantinformation. The user information and the merchant information mentionedhere can refer to the credit rating of the user or the merchant, theexisting carbon-saving bonus point in the carbon-saving account, etc.Other ways to determine the confidence coefficient are omitted here.

To further describe the data processing method provided in the presentspecification, an entire data processing process is described by using aspecific example below, as shown in FIG. 3.

FIG. 3 is a schematic diagram illustrating an entire data processingprocess, according to an implementation of the present specification.

As shown in FIG. 3, a user can obtain a certain reward by collectingimages of carbon-saving behavior performed by a merchant, and themerchant can obtain a certain carbon-saving bonus point after the usercollects an image to be identified of the merchant. Therefore, theincentive mechanism can stimulate more merchants to implement positivecarbon-saving behavior, which forms a virtuous cycle and has positiveimpacts on energy conversation and emission reduction in the society.

The data processing methods provided in one or more implementations ofthe specification are described above. Based on the same idea, thespecification further provides corresponding data processing apparatus,as shown in FIG. 4 and FIG. 5.

FIG. 4 is a schematic diagram illustrating a data processing apparatus,according to an implementation of the present specification. Theapparatus includes: an acquisition module 401, configured to obtain animage to be identified sent by a user, where the image to be identifiedis collected by the user for a merchant; an identification module 402,configured to identify the image to be identified by using a pre-trainedimage identification model, to determine carbon-saving behavior of themerchant; and a determining module 403, configured to determine, basedon the carbon-saving behavior, a value representing a carbon-savingamount saved by the merchant.

The apparatus further includes: a receiving module 404, configured to:when the carbon-saving behavior of the merchant is not identified fromthe image to be identified by using the image identification model,receive labeled information that corresponds to the image to beidentified and that is sent by the user, where the labeled informationcomprises the carbon-saving behavior of the merchant labeled by the userfor the image to be identified; the determining module 403 is configuredto determine, based on the received labeled information, the valuerepresenting the carbon-saving amount saved by the merchant; and thedetermining module 403 is configured to determine the carbon-savingbehavior included in the labeled information; determine a referencevalue that corresponds to the carbon-saving behavior; and determine,based on the product of the reference value and a confidencecoefficient, the value representing the carbon-saving amount saved bythe merchant.

The apparatus further includes: an adjustment module 405, configured toadjust the image identification model based on the image to beidentified and the labeled information; and the determining module 403is configured to determine a contribution degree of the user, andproviding the user with a virtual item that corresponds to thecontribution degree, and/or adding the contribution degree to an accountof the user.

The value representing the carbon-saving amount saved by the merchantincludes a carbon-saving bonus point representing the carbon-savingamount saved by the merchant.

The apparatus further includes: an addition module 406, configured toadd the carbon-saving bonus point to a carbon-saving account of themerchant.

FIG. 5 is a schematic diagram illustrating a data processing apparatus,according to an implementation of the present specification. Theapparatus includes: a collecting module 501, configured to collect animage of a merchant as an image to be identified; and a sending module502, configured to send the image to be identified to a server, so theserver identifies carbon-saving behavior of the merchant from the imageto be identified by using a pre-trained image identification model, anddetermines, based on the carbon-saving behavior, a value representing acarbon-saving amount saved by the merchant.

The apparatus further includes: a receiving module 503, configured toreceive a labeling prompt sent by the server, where the labeling promptis sent to a user when the server does not identify the carbon-savingbehavior of the merchant from the image to be identified by using theimage identification model; and the sending module 502 is configured toreceive labeled information input by the user based on the labelingprompt, and send the labeled information to the server, so the serverdetermines the carbon-saving behavior of the merchant based on thelabeled information.

As shown in FIG. 6, the present specification further correspondinglyprovides a data processing equipment based on the previous dataprocessing method. The equipment includes one or more memories andprocessors, and the memory stores a program that is executed by the oneor more processors to perform the following steps: obtaining an image tobe identified sent by a user, where the image to be identified iscollected by the user for a merchant; identifying the image to beidentified by using a pre-trained image identification model, toidentify carbon-saving behavior of the merchant; and determining, basedon the carbon-saving behavior, a value representing a carbon-savingamount saved by the merchant.

As shown in FIG. 7, the present specification further correspondinglyprovides a data processing equipment based on the previous dataprocessing method. The equipment includes one or more memories andprocessors, and the memory stores a program that is executed by the oneor more processors to perform the following steps: collecting an imageof a merchant as an image to be identified; and sending the image to beidentified to a server, so the server identifies carbon-saving behaviorof the merchant from the image to be identified by using a pre-trainedimage identification model, and determines, based on the carbon-savingbehavior, a value representing a carbon-saving amount saved by themerchant.

In one or more implementations of the present specification, after theimage to be identified collected by the user for the merchant isobtained, the image to be identified can be identified by using thepre-trained image identification model to identify the carbon-savingbehavior of the merchant, and the value representing the carbon-savingamount saved by the merchant can be determined based on the identifiedcarbon-saving behavior.

The carbon-saving behavior of the merchant can be identified based onthe image to be identified collected by the user for the merchant, andthe carbon-saving of the merchant can be effectively quantified based onthe identified carbon-saving behavior of the merchant. Therefore, themerchant can understand the actual carbon-saving condition of themerchant based on the quantified value of carbon-saving, and caneffectively carry out the subsequent carbon-saving work based on theknown actual carbon-saving condition. These actions have positiveimpacts on energy conservation and emission reduction in the society.

In the 1990s, whether a technical improvement is a hardware improvement(for example, an improvement to circuit structures, such as a diode, atransistor, or a switch) or a software improvement (an improvement to amethod procedure) can be clearly distinguished. However, as technologiesdevelop, current improvements to many method procedures can beconsidered as direct improvements to hardware circuit structures. Adesigner usually programs an improved method procedure into a hardwarecircuit to obtain a corresponding hardware circuit structure. Therefore,a method procedure can be improved by using a hardware entity module.For example, a programmable logic device (PLD) (for example, a fieldprogrammable gate array (FPGA)) is such an integrated circuit, and alogical function of the PLD is determined by a user through deviceprogramming. The designer performs programming to “integrate” a digitalsystem to a PLD without requesting a chip manufacturer to design andproduce an application-specific integrated circuit chip. In addition, atpresent, instead of manually manufacturing an integrated circuit chip,such programming is mostly implemented by using “logic compiler”software. The logic compiler software is similar to a software compilerused to develop and write a program. Original code also needs to bewritten in a particular programming language for compilation. Thelanguage is referred to as a hardware description language (HDL). Thereare many types of HDLs, such as the Advanced Boolean Expression Language(ABEL), the Altera Hardware Description Language (AHDL), Confluence, theCornell University Programming Language (CUPL), HDCal, the Java HardwareDescription Language (JHDL), Lava, Lola, MyHDL, PALASM, and the RubyHardware Description Language (RHDL). The very-high-speed integratedcircuit hardware description language (VHDL) and Verilog are mostcommonly used. A person skilled in the art should also understand that ahardware circuit that implements a logical method procedure can bereadily obtained once the method procedure is logically programmed byusing the several described hardware description languages and isprogrammed into an integrated circuit.

A controller can be implemented by using any appropriate method. Forexample, the controller can be a microprocessor or a processor, or acomputer-readable medium that stores computer readable program code(such as software or firmware) that can be executed by themicroprocessor or the processor, a logic gate, a switch, anapplication-specific integrated circuit (ASIC), a programmable logiccontroller, or a built-in microprocessor. Examples of the controllerinclude but are not limited to the following microprocessors: ARC 625D,Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. Thememory controller can also be implemented as a part of the control logicof the memory. A person skilled in the art also knows that, in additionto implementing the controller by using the computer readable programcode, logic programming can be performed on method steps to allow thecontroller to implement the same function in forms of the logic gate,the switch, the application-specific integrated circuit, theprogrammable logic controller, and the built-in microcontroller.Therefore, the controller can be considered as a hardware component, andan apparatus configured to implement various functions in the controllercan also be considered as a structure in the hardware component. Or theapparatus configured to implement various functions can even beconsidered as both a software module implementing the method and astructure in the hardware component.

The system, apparatus, module, or unit illustrated in the previousimplementations can be implemented by a computer chip or an entity, orcan be implemented by a product with a specified function. A typicalimplementation device is a computer. The computer can be, for example, apersonal computer, a laptop computer, a cellular phone, a camera phone,a smartphone, a personal digital assistant, a media player, a navigationdevice, an email device, a game console, a tablet computer, or awearable device, or a combination of any of these devices.

For ease of description, the apparatus above is described by dividingfunctions into various units. Certainly, when the present specificationis implemented, a function of each unit can be implemented in one ormore pieces of software and/or hardware.

A person skilled in the art should understand that the implementation ofthe present specification can be provided as a method, a system, or acomputer program product. Therefore, the present specification can use aform of hardware only implementations, software only implementations, orimplementations with a combination of software and hardware. Inaddition, the present specification can use a form of a computer programproduct that is implemented on one or more computer-usable storage media(including but not limited to a disk memory, a CD-ROM, an opticalmemory, etc.) that include computer-usable program code.

The present application is described with reference to the flowchartsand/or block diagrams of the method, the device (system), and thecomputer program product based on one or more implementations of thepresent application. It is worthwhile to note that computer programinstructions can be used to implement each process and/or each block inthe flowcharts and/or the block diagrams and a combination of a processand/or a block in the flowcharts and/or the block diagrams. Thesecomputer program instructions can be provided for a general-purposecomputer, a dedicated computer, an embedded processor, or a processor ofanother programmable data processing equipment to generate a machine, sothe instructions executed by the computer or the processor of theanother programmable data processing equipment generate a device forimplementing a specific function in one or more processes in theflowcharts and/or in one or more blocks in the block diagrams.

These computer program instructions can be stored in a computer readablememory that can instruct the computer or the another programmable dataprocessing equipment to work in a specific way, so the instructionsstored in the computer readable memory generate an artifact thatincludes an instruction apparatus. The instruction apparatus implementsa specific function in one or more processes in the flowcharts and/or inone or more blocks in the block diagrams.

These computer program instructions can be loaded onto the computer oranother programmable data processing equipment, so a series ofoperations and operations and steps are performed on the computer or theanother programmable device, thereby generating computer-implementedprocessing. Therefore, the instructions executed on the computer or theanother programmable device provide steps for implementing a specificfunction in one or more processes in the flowcharts and/or in one ormore blocks in the block diagrams.

In a typical configuration, a calculating device includes one or moreprocessors (CPU), an input/output interface, a network interface, and amemory.

The memory can include a non-persistent memory, a random access memory(RAM), a non-volatile memory, and/or another form that are in a computerreadable medium, for example, a read-only memory (ROM) or a flash memory(flash RAM). The memory is an example of the computer readable medium.

The computer readable medium includes persistent, non-persistent,movable, and unmovable media that can store information by using anymethod or technology. The information can be a computer readableinstruction, a data structure, a program module, or other data. Examplesof a computer storage medium include but are not limited to a phasechange memory (PRAM), a static random access memory (SRAM), a dynamicrandom access memory (DRAM), another type of random access memory (RAM),a read-only memory (ROM), an electrically erasable programmableread-only memory (EEPROM), a flash memory or another memory technology,a compact disc read-only memory (CD-ROM), a digital versatile disc (DVD)or another optical storage, a cassette magnetic tape, a magnetictape/magnetic disk storage or another magnetic storage device. Thecomputer storage medium can be used to store information accessible bythe calculating device. Based on the definition in the presentspecification, the computer readable medium does not include transitorycomputer readable media (transitory media) such as a modulated datasignal and carrier.

It is worthwhile to further note that, the terms “include”, “comprise”,or their any other variants are intended to cover a non-exclusiveinclusion, so a process, a method, a product or a device that includes alist of elements not only includes those elements but also includesother elements which are not expressly listed, or further includeselements inherent to such process, method, product or device. Withoutmore constraints, an element preceded by “includes a . . . ” does notpreclude the existence of additional identical elements in the process,method, product or device that includes the element.

The present specification can be described in the general context ofcomputer executable instructions executed by a computer, for example, aprogram module. Generally, the program module includes a routine, aprogram, an object, a component, a data structure, etc. executing aspecific task or implementing a specific abstract data type. The one ormore implementations of the present specification can also be practicedin distributed computing environments. In these distributed computingenvironments, tasks are executed by remote processing devices that areconnected by using a communications network. In a distributed computingenvironment, the program module can be located in both local and remotecomputer storage media including storage devices.

The implementations in the present specification are all described in aprogressive method. For same or similar parts in the implementations,refer to these implementations. Each implementation focuses on adifference from other implementations. Particularly, a systemimplementation is basically similar to a method implementation, andtherefore, is described briefly. For related parts, references can bemade to related descriptions in the method implementation.

Specific implementations of the present application are described above.Other implementations fall within the scope of the appended claims. Insome situations, the actions or steps described in the claims can beperformed in an order different from the order in the implementationsand the desired results can still be achieved. In addition, the processdepicted in the accompanying drawings is not necessarily shown in aparticular order or a consecutive order to achieve the desired results.In some implementations, multi-tasking and parallel processing are alsopossible or can be advantageous.

The previous descriptions are merely one or more implementations of thepresent specification, and are not intended to limit the presentspecification. For a person skilled in the art, the one or moreimplementations of the present specification can have variousmodifications and changes. Any modifications, equivalent substitutions,and improvements made within of the spirit and the principle of one ormore implementations of the present specification shall fall within thescope of the claims in the present specification.

What is claimed is:
 1. A method, comprising: obtaining an image sent bya user, wherein the image is collected by the user for a merchant;attempting to identify one or more behaviors of the merchant that relateto reducing carbon footprint, comprising processing the image using atrained image recognition model; in response to determining that theattempt to identify the one or more behaviors of the merchant thatrelate to reducing carbon footprint is successful; determining, based onthe one or more behaviors that relate to reducing carbon footprint, anumeric value that is an estimate of a change in carbon footprint of themerchant; or in response to determining that the attempt to identify theone or more behaviors of the merchant that relate to reducing carbonfootprint is unsuccessful; prompting the user to enter labelinginformation for the image that identifies respective user-specifiedtypes of the one or more behaviors of the merchant that relate toreducing carbon footprint; determining, based on the labelinginformation entered by the user, the numeric value that is the estimateof the change in carbon footprint of the merchant; and adjusting, usingthe labeling information entered by the user, the trained imagerecognition model; and crediting an account associated with the merchantby an amount corresponding to the numeric value.
 2. The method accordingto claim 1, wherein the determining, based on the labeling informationentered by the user, the numeric value that is the estimate of thechange in carbon footprint of the merchant comprises: determining thebehavior that relates to reducing carbon footprint and that is specifiedby the labeling information; determining a reference value thatcorresponds to the behavior that is specified by the labelinginformation; and determining, based on computing a product of thereference value and a confidence coefficient, the numeric value that isthe estimate of the change in carbon footprint of the merchant.
 3. Themethod according to claim 1, wherein the method further comprises:determining a contribution degree of the user; and performing at leastone of providing the user with a virtual item that corresponds to thecontribution degree, or crediting an account of the user by thecontribution degree.
 4. The method according to claim 1, wherein theaccount associated with the merchant comprises a bonus point account;and the method further comprises: adding bonus points that correspond tothe numeric value to the bonus point account of the merchant.
 5. Anon-transitory, computer-readable medium storing one or moreinstructions executable by a computer system to perform operationscomprising: obtaining an image sent by a user, wherein the image to iscollected by the user for a merchant; attempting to identify one or morebehaviors of the merchant that relate to reducing carbon footprint,comprising processing the image using a trained image recognition model;in response to determining that the attempt to identify the one or morebehaviors of the merchant that relate to reducing carbon footprint issuccessful; determining, based on the one or more behaviors that relateto reducing carbon footprint, a numeric value that is an estimate of achange in carbon footprint of the merchant; or in response todetermining that the attempt to identify the one or more behaviors ofthe merchant that relate to reducing carbon footprint is unsuccessful;prompting the user to enter labeling information for the image thatidentifies respective user-specified types of the one or more behaviorsof the merchant that relate to reducing carbon footprint; determining,based on the labeling information entered by the user, the numeric valuethat is the estimate of the change in carbon footprint of the merchant,and adjusting, using the labeling information entered by the user, thetrained image recognition model, and crediting an account associatedwith the merchant by an amount corresponding to the numeric value. 6.The non-transitory, computer-readable medium according to claim 5,wherein the determining, based on the labeling information entered bythe user, the numeric value that is the estimate of the change in carbonfootprint of the merchant comprises: determining the behavior thatrelates to reducing carbon footprint and that is specified by thelabeling information; determining a reference value that corresponds tothe behavior that is specified by the labeling information; anddetermining, based on computing a product of the reference value and aconfidence coefficient, the numeric value that is the estimate of thechange in carbon footprint of the merchant.
 7. The non-transitory,computer-readable medium according to claim 5, wherein the operationsfurther comprise: determining a contribution degree of the user; andperforming at least one of providing the user with a virtual item thatcorresponds to the contribution degree, or crediting an account of theuser by the contribution degree.
 8. The non-transitory,computer-readable medium according to claim 5, wherein the accountassociated with the merchant comprises a bonus point account; and theoperations further comprise: adding bonus points that correspond to thenumeric value to the bonus point account of the merchant.
 9. Acomputer-implemented system, comprising: one or more computers; and oneor more computer memory devices interoperably coupled with the one ormore computers and having tangible, non-transitory, machine-readablemedia storing one or more instructions that, when executed by the one ormore computers, perform one or more operations comprising: obtaining animage sent by a user, wherein the image is collected by the user for amerchant; attempting to one or more behaviors of the merchant thatrelate to reducing carbon footprint, comprising processing the imageusing a trained image recognition model; in response to determining thatthe attempt to identify the one or more behaviors of the merchant thatrelate to reducing carbon footprint is successful; determining, based onthe one or more behaviors that relate to reducing carbon footprint, anumeric value that is an estimate of a change in carbon footprint of themerchant; or in response to determining that the attempt to identify theone or more behaviors of the merchant that relate to reducing carbonfootprint is unsuccessful; prompting the user to enter labelinginformation for the image that identifies respective user-specifiedtypes of the one or more behaviors of the merchant that relate toreducing carbon footprint; determining, based on the labelinginformation entered by the user, the numeric value that is estimate ofthe change in carbon footprint of the merchant, and adjusting, using thelabeling information entered by the user, the trained image recognitionmodel, and crediting an account associated with the merchant by anamount corresponding to the numeric value.
 10. The computer-implementedsystem according to claim 9, wherein the determining, based on thelabeling information entered by the user, the numeric value that is theestimate of the change in carbon footprint of the merchant comprises:determining the behavior that relates to reducing carbon footprint andthat is specified by the labeling information; determining a referencevalue that corresponds to the behavior that is specified by the labelinginformation; and determining, based on computing a product of thereference value and a confidence coefficient, the numeric value that isthe estimate of the change in carbon footprint of the merchant.
 11. Thecomputer-implemented system according to claim 9, wherein the operationsfurther comprise: determining a contribution degree of the user; andperforming at least one of providing the user with a virtual item thatcorresponds to the contribution degree, or crediting an account of theuser by the contribution degree.
 12. The computer-implemented systemaccording to claim 9, wherein the account associated with the merchantcomprises a bonus point account; and the operations further comprise:adding bonus points that correspond to the numeric value to the bonuspoint account of the merchant.